Methods and apparatus to associate audience members with over-the-top device media impressions

ABSTRACT

Methods and apparatus to associate audience members with over-the-top device media impressions. An example audience measurement apparatus includes an impression monitoring system to monitor and log media impressions based on impression requests received via network communications from over the top devices. A linkage database system is to implement the impression monitoring system. A processor in circuit with the impression monitoring system is to: generate Internet protocol (IP) address-to-cookie mappings; associate household identifiers of households with ones of the IP address-to-cookie mappings; and associate ones of the household identifiers to the logged media impressions based on IP addresses associated with the media impressions and based on the household identifiers associated with the ones of the IP address-to-cookie mappings.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent claims the benefit of U.S. Provisional Application62/441,238, filed on Dec. 31, 2016 and U.S. Provisional Application62/455,406, filed on Feb. 6, 2017, both of which are hereby incorporatedherein by reference in their entireties.

BACKGROUND

Audience measurement entities (AMEs) and/or other businesses oftendesire to link demographics to monitoring information. An AME typicallyestablishes a panel of users who have agreed to provide theirdemographic information and to have their media exposure activitiesmonitored. A panel-based approach to monitoring media content iseffective with traditional viewing channels (e.g., cable Television.Broadcast Television, etc.). However, with the fragmentation of viewingplatforms (e.g., streaming, internet based media, etc.), employing apanel-based approach for monitoring media may not be a cost-effectiveapproach due to the large number of panelists that would be required tomonitor accesses to the media.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example audience measurement system constructed inaccordance with the teachings of this disclosure.

FIG. 2A shows an example linkage database system of the example audiencemeasurement system of FIG. 1.

FIG. 2B shows another example linkage database system disclosed hereinthat may be used to implement the audience measurement system of FIG. 1.

FIG. 2C is a table illustrating example household compositioninformation.

FIG. 3 is an example flowchart representative of example machinereadable instructions for implementing the audience measurement systemof FIGS. 1, 2A and 2B.

FIGS. 4-5 are flowcharts representative of example machine readableinstructions for implementing the example linkage database systems ofFIGS. 1, 2A and 2B.

FIGS. 6-7 are flowcharts representative of example machine readableinstructions for implementing an impression monitoring system of theexample audience measurement system of FIGS. 1, 2A and 2B.

FIG. 8 illustrates an example OTT demographic assignment processdisclosed herein.

FIG. 9A illustrates an example process of the example OTT demographicassignment process of FIG. 8 for mapping a residential IP addresses todatabase proprietor cookies.

FIG. 9B illustrates an example communication flow to map a residentialIP address to a database proprietor cookie.

FIG. 10 illustrates an example process of the example OTT demographicassignment process of FIG. 8 for filtering out low-quality IPaddress-to-cookie mappings.

FIG. 11 illustrates an example process of the example OTT demographicassignment process of FIG. 8 for mapping a database proprietor householdID to a corresponding IP address.

FIG. 12A illustrates an example OTT demographic assignment process ofFIG. 8 to assign household demographics provided by a databaseproprietor to OTT media impressions.

FIG. 12B is an example communications flow for assigning householddemographics to OTT media impressions.

FIG. 13 is an overall audience measurement system implemented by anaudience measurement entity and a database proprietor.

FIG. 14 is an overview of estimating household composition forhouseholds that do not have database proprietor householdidentifications.

FIGS. 15-19 provide examples of determining an estimated householdcomposition for an unknown household based on known householdcompositions of households in the same IP address range as an IP addressof the unknown household.

FIG. 20 is a block diagram of an example processor platform capable ofexecuting instructions to implement the example methods and apparatusdisclosed herein.

DETAILED DESCRIPTION

Audience measurement entities (AMEs) and/or other businesses oftendesire to link demographics to monitoring information. Audiencemeasurement entities (AMEs), as used herein, may include ratingagencies, or entities interested in tracking media viewing impressionssuch as, for example, The Nielsen Company. As used herein, the term“media” includes any type of content and/or advertisement (e.g., audioand/or video (still or moving) content and/or advertisement) deliveredvia any type of media distribution medium or media delivery platform.Thus, media includes television programming, television advertisements,radio programming, radio advertisements, movies, web sites, streamingmedia, television commercials, radio commercials. Internet ads, etc. AnAME typically establishes a panel of users who have agreed to providetheir demographic information and to have their media exposureactivities monitored.

A panel-based approach to monitoring media content is effective withtraditional viewing channels (e.g., cable Television, BroadcastTelevision, etc.). However, with the fragmentation of viewing options(e.g., streaming, internet based media, etc.), employing a panel-basedapproach for monitoring media may not be a cost-effective approach dueto the large number of panelists that would be required to monitor themedia. For example, a relatively large panel of users may be required tomonitor over-the-top media delivered over the Internet. Thus, to monitorover-the-top media, audience measurement entities typically employ acensus-based approach. A census-based approach monitors media accessactivities regardless of whether the audience members are panelists. Assuch, the AME does not have collected demographics about many of theaudience members corresponding to the census-based measurements. Assuch, although a census-based approach establishes volumetric metricsneeded for over-the-top media monitoring, the census-based approach doesnot provide direct demographic information of viewers associated withthe over-the-top media. Example methods, apparatus and articles ofmanufacture disclosed herein establish and/or improve demographicinformation when leveraging a census-based solution to monitor ratingsof over-the-top media.

The inventions disclosed in Blumenau, U.S. Pat. No. 6,108,637, which ishereby incorporated herein by reference in its entirety, fundamentallychanged the way Internet monitoring is performed and overcame thelimitations of the server-side log monitoring techniques describedabove. For example, Blumenau disclosed a technique wherein Internetmedia to be tracked is tagged with monitoring instructions. Inparticular, monitoring instructions (also known as a media impressionrequest) are associated with the hypertext markup language (HTML) of themedia to be tracked. When a client requests the media, both the mediaand the impression request are downloaded to the client. The impressionrequests are, thus, executed whenever the media is accessed, be it froma server or from a cache. Additional techniques to monitorInternet-based media accesses are disclosed by Mazumdar et al. in U.S.Pat. No. 8,370,489, which is hereby incorporated herein by reference inits entirety. Further, additional techniques to monitor mobile-basedmedia and/or employing activity assignment model analyzers to generatemedia measurement reports is provided in U.S. patent application Ser.No. 14/569,474 (Rao et al.), which is incorporated herein by referencein its entirety.

Impression requests cause monitoring data reflecting information aboutan access to the media to be sent from the client that downloaded themedia to a monitoring entity. Sending the monitoring data from theclient to the monitoring entity is known as an impression request.Typically, the monitoring entity is an audience measurement entity (AME)that did not provide the media to the client and who is a trusted (e.g.,neutral) third party for providing accurate usage statistics (e.g., TheNielsen Company, LLC). Advantageously, because the impression requestsare associated with the media and executed by the client browserwhenever the media is accessed, the monitoring information is providedto the AME (e.g., via an impression request) irrespective of whether theclient corresponds to a panelist of the AME.

There are many database proprietors operating on the Internet. Thesedatabase proprietors provide services to large numbers of subscribers.In exchange for the provision of services, the subscribers register withthe database proprietors. Examples of such database proprietors includesocial network sites (e.g., Facebook, Twitter. MySpace, etc.),multi-service sites (e.g., Yahoo!, Google, Axiom, Catalina, etc.),online retailer sites (e.g., Amazon.com. Buy.com, etc.), creditreporting sites (e.g., Experian), streaming media sites (e.g., YouTube,etc.), etc. These database proprietors set cookies and/or otherdevice/user identifiers on the client devices of their subscribers toenable the database proprietor to recognize their subscribers when theyvisit the database proprietor website.

The protocols of the Internet make cookies inaccessible outside of thedomain (e.g., Internet domain, domain name, etc.) on which they wereset. Thus, a cookie set in, for example, the amazon.com domain isaccessible to servers in the amazon.com domain, but not to serversoutside that domain. Therefore, although an AME might find itadvantageous to access the cookies set by the database proprietors, theyare unable to do so.

The inventions disclosed in Mainak et al., U.S. Pat. No. 8,370,489,which is incorporated by reference herein in its entirety, enable an AMEto leverage the existing databases of database proprietors to collectmore extensive Internet usage by extending the impression requestprocess to encompass partnered database proprietors and by using suchpartners as interim data collectors.

As used herein, an impression is defined to be an event in which a homeor individual accesses and/or is exposed to media (e.g., anadvertisement, content, a group of advertisements and/or a collection ofcontent). In Internet advertising, a quantity of impressions orimpression count is the total number of times media (e.g., content, anadvertisement or advertisement campaign) has been accessed by a webpopulation (e.g., the number of times the media is accessed). In someexamples, an impression or media impression is logged by an impressioncollection entity (e.g., an AME or a database proprietor) in response toa beacon request from a user/client device that requested the media.

As used herein, a demographic impression is a media impression logged byan entity with corresponding demographic information of a householdand/or audience member associated with the media impression. A panelistdemographic impression is a media impression logged by an AME for whichthe AME has panelist demographics corresponding to a household and/oraudience member exposed to media. As used herein, a database proprietordemographic impression is an impression recorded by a databaseproprietor in association with corresponding demographic informationprovided by the database proprietor in response to a beacon request froma client device of a registered subscriber of the database proprietor.In some examples, a media impression is not associated withdemographics.

Unlike computers, tablets and/or other internet-based devices, OTTdevices do not employ cookies. Thus, an audience measurement entity(AME) cannot employ a cookie to enable the audience measurement entityto identify an OTT device whenever the OTT device is used to accessmedia. In examples disclosed herein, an audience measurement entityreceives the IP address information from OTT devices. However, withoutlinking the IP address information to corresponding audience members,the AME cannot determine demographic information of a viewer using theOTT device.

Example methods, apparatus and articles of manufacture disclosed hereininclude associating demographics to media impressions corresponding tomedia accessed via over-the-top (OTT) devices. For example, disclosedexamples include mapping public Internet protocol (IP) addresses ofhouseholds having OTT devices with household IDs of such households. Thehousehold IDs are maintained by a database proprietor in associationwith demographic information about those households. In this manner, anAME can associate OTT device-based impressions (e.g., OTT impressions)with corresponding demographic information based on the IP addressesreceived with impression requests (e.g., messages reporting occurrencesof media impressions) from OTT devices and the IP address-to-householdID mapping. Example techniques that may be used to implement OTT mediaimpression collection are disclosed in Splaine et al. (U.S. patentapplication Ser. No. 14/823,621), which is hereby incorporated herein byreference in its entirety.

Example methods, apparatus and articles of manufacture disclosed hereinemploy rich data collected by one or more database proprietors todetermine demographic information for impressions of media accessed viaOTT devices. For example, the methods, apparatus and articles ofmanufacture disclosed herein enable impression monitoring from OTTdevices regardless of whether a viewer associated with media presentedby the OTT device is registered as a panelist with an audiencemeasurement entity. For example, when an OTT device transmitsimpressions to an impression monitoring system of an AME disclosedherein, the AME can determine demographic information relating to theviewer of the content presented by the OTT device based on an IP addressassociated with the OT device (e.g., regardless of whether the viewer isregistered with the audience measurement entity). Thus, an audiencemeasurement system disclosed herein monitors impressions transmitted byan OTT device by using an IP address (e.g., a residential IP address)associated with the OTT impressions.

To enable an audience measurement entity to monitor impressions using anIP address associated with an OTT device (e.g., that is not registeredwith the audience measurement entity), the example methods, apparatusand articles of manufacture disclosed herein employ one or more databaseproprietors (e.g., Facebook. Experian, Google, etc.). More specifically,prior to monitoring an impression event, the audience measurement entityestablishes (e.g., maps) a cookie and/or a household identificationassociated with a database proprietor to the IP address associated withan OTT device. Subsequently, during an impression monitoring phase, theaudience measurement entity retrieves demographic information of ahousehold from the database proprietor based on the previouslyestablished mapping of the database proprietor cookie and/or householdidentification to the IP address of the OTT device. In other words, theaudience measurement entity employs example impression monitoringsystems disclosed herein that associate household demographicinformation collected and stored by a database proprietor to establishor formulate a ratings report (e.g., a digital content ratings report)based on an IP address associated with an OTT device presenting media.In some examples, example impression monitoring systems disclosed hereinmay consult a census and/or panelist monitoring database maintained bythe audience monitoring system to verify, correlate, analyze and/orimprove demographic information obtained from the database proprietor.In some examples, example impression monitoring systems disclosed hereinenable determining user-level viewership of OTT devices.

In some examples, a database proprietor may not have householddemographic information (e.g., a household identification) associatedwith an IP address of an OTT device presenting media. Thus, in suchexamples, the audience measurement entity cannot directly map householddemographic information to an IP address of a household. In some suchexamples, the audience measurement entity estimates a representativecomposition of a household for IP addresses of OTT devices that cannotbe mapped to database proprietor household demographic information(e.g., a household identification). In some examples, the audiencemeasurement entity establishes the estimated household composition priorto monitoring an impression event. In some such examples, the impressionmonitoring system of the audience measurement entity employs theestimated household composition to establish or formulate a ratingsreport (e.g., a digital content ratings report) based on an IP addressassociated with an OTT device presenting media (i.e., for an IP addressthat does not have database proprietor household demographicinformation).

FIG. 1 illustrates an example audience measurement system 100constructed in accordance with the teachings of this disclosure. Theaudience measurement system 100 of the illustrated example includes animpression monitoring system 102 and a linkage database system 104 thatprovides information to implement the impression monitoring system 102.For example, the impression monitoring system 102 of the illustratedexample can be used to determine a household member composition (e.g.,demographics, number of adults, number of children, ages, genders,household income, primary spoken language (e.g., Spanish, English, etc.)of a household corresponding to an OTT impression. The linkage databasesystem 104 of the illustrated example enables the impression monitoringsystem 102 to associate one or more particular household audiencemembers of the household with the OTT impression based on the householdcomposition.

The example audience measurement system 100 may be employed by anaudience measurement entity (AME) (e.g., the AME 201 of FIGS. 2A and2B). For example, the impression monitoring system 102 and/or thelinkage database system 104 may be implemented by the AME. The AME maybe a neutral third party (such as The Nielsen Company (US). LLC) thatdoes not source, create, and/or distribute media and can, thus, provideunbiased ratings and/or other media monitoring statistics or reports.

The audience measurement system 100 of the illustrated example maymonitor media accessed at a media presentation environment 108. Themedia presentation environment 108 of the illustrated example of FIG. 1is a home location or a household. In the illustrated example, the mediapresentation environment 108 includes a residential gateway 110 that isconnected to the Internet 112 via an internet service provider (ISP) 114(e.g., a cable internet provider, a digital subscriber line (DSL)provider, etc.)). The example residential gateway 110 of the illustratedexample of FIG. 1 includes a router that enables multiple devices withinthe media presentation environment 108 to communicate via the Internet112. The residential gateway 110 may host a wireless local area network(LAN) using, for example. WiFi. However, any other past, present, and/orfuture approach to hosting a local area network may additionally oralternatively be used.

The ISP 114 typically assigns a single public Internet Protocol (IP)address 111 (e.g., a dynamic or static IP address) per mediapresentation environment 108 (e.g., a household). As used herein, thepublic IP address 111 is assigned to the residential gateway 110 of themedia presentation environment 108 by the ISP 114 and is deemed aspublic because it is used to uniquely identify the residential gateway110 on the public Internet 112. The public IP address 111 is shared byclient devices that are at the media presentation environment 108 andcommunicating via the ISP 114 in that network communications from/to theclient devices are routed across the Internet 112 using the public IPaddress 111. As such, when an Internet connection is shared by multipledevices (e.g., via a wireless access point, via a router, etc.) thosemultiple devices use the same public IP address 111 to communicate overthe Internet 112.

For example, within the LAN hosted by the example residential gateway110, individual devices such as, for example, a client device 116 and/oran OTT device 118 connect to the Internet 112 via the residentialgateway 110. The example client device 116 of FIG. 1 may be any devicecapable of accessing media over the Internet 112. For example, theclient device 116 may be a computer, a tablet, a smart television,and/or any other Internet-capable device or appliance. Examples of theOTT device 118 include, for example, a video game console (e.g., Xbox®,PlayStation®), a smart television, a digital media player (e.g., a Roku®media player, a Slingbox®, Apple® T.V., etc.), and/or any other devicethat may stream media (e.g., video content, audio content, etc.) via theinternet 112.

In some examples, the individual devices within the media presentationenvironment 108 may be assigned respective private Internet Protocol(IP) addresses by the residential gateway 110. In the illustratedexample, the private IP addresses may be assigned using a Dynamic HostConfiguration Protocol (DHCP). When a device within the LAN transmits arequest (e.g., a request for media) to a resource outside the LAN (e.g.,on the Internet 112), the example residential gateway 110 translates theoriginating private IP address of the device making the request to thepublic IP address 111 of the example residential gateway 110 beforerelaying the request outside the LAN (e.g. to the destination). Thus,when the resource outside the LAN receives the request, the resource cantransmit a return response to the residential gateway 110 using thepublic IP address 111. On the return path, the example residentialgateway 110 translates the destination IP address of the response to theprivate IP address of the requesting device so that the return messagemay be delivered to the device (e.g., the client device 116 or the OTTdevice 118) that made the original request. Thus, outside of the LAN,the client devices of the media presentation environment 108 thatcommunicate via the residential gateway 110 are identified with thepublic IP address 111 of the residential gateway 110 for networkcommunications on the Internet 112.

To monitor impressions (e.g., create the ratings and/or other mediamonitoring statistics 119) for media impressions presented by the OTTdevice 118, the audience measurement system 100 of the illustratedexample employs the impression monitoring system 102. More specifically,the impression monitoring system 102 of the illustrated example monitorsimpressions of media accessed via the OTT device 118. Because OTTdevices generally do not employ cookies, the impression monitoringsystem 102 of the illustrated example monitors for impressions of theOTT device 118 based on the IP address 111 of the residential gateway110. Thus, the impression monitoring system 102 of the illustratedexample monitors impressions by using the IP address 111 of theresidential gateway 110 associated with the OTT device 118.

The impression monitoring system 102 of the illustrated example includesdata collection facilities (e.g., servers). For example, the impressionmonitoring system 102 of the illustrated example includes an examplecensus collector 120 (e.g., a central data collection server), anexample harmonization system 122 (e.g., a harmonization operator) and anexample demographic determiner 124 (e.g., a demographic classifier) thatmay be operated by the AME. In some examples, these data collectionfacilities are structured in a tiered approach with many satellitecollection facilities collecting data and forwarding the same to one ormore collection facilities.

In the illustrated example, the OTT device 118 transmits monitoringinformation or impression requests (e.g., that the residential gateway110 relays with the IP address 111) to the census collector 120 of theexample impression monitoring system 102. For example, when media isaccessed via the OTT device 118, the OTT device 118 of the illustratedexample sends an impression request or an impression reporting messageto the census collector 120 via the residential gateway 110. Theresidential gateway 110 associates the IP address 111 with theimpression reporting message as a source IP address when the residentialgateway 110 relays the impression reporting message to the censuscollector 120. For example, the OTT device 118 may access media (e.g., avideo game, programming, video, webpages, etc.) via the Internet 112. Inthe illustrated example, the OTT device 118 executes instructions toprovide the impression request (e.g., a digital content ratings ping orrequest) to the census collector 120 of the AME when the OTT device 118presents and/or accesses media. In some examples, an examplemanufacturer (e.g., Sony®, Microsoft®, etc.) of the OTT device 118 maycooperate with the AME to configure the OTT device 118 to provide thetransmission request (e.g., provide impression monitoring information)to the census collector 120 of the AME (e.g., by sending a networkcommunication to a Uniform Resource Locator (URL) address of a severthat implements the census collector 120) when the OTT device 118accesses media via the Internet 112.

For example, the OTT device 118 of the illustrated example may haveinstructions that are locally stored on the OTT device 118 that, whenexecuted by a processor of the OTT device 118, cause the OTT device 118to send an impression reporting message to the census collector 120 viathe Internet 112 when media is accessed via the Internet 112 via the OTTdevice 118. Thus, an example manufacturer may implement reportingfunctionality in the OTT device 118. For example, the OTT device 118 ofthe illustrated example may be implemented according to a softwaredevelopment kit (SDK) provided to the manufacturer of the OTT device 118that includes instructions from the AME that are to cause the impressionreporting by the OTT device 118. In such an example, the manufacturer ofthe OTT device 118 includes the instructions (e.g., executable code)provided as part of the SDK in the OTT device 118 to implement theimpression reporting features of the example OTT device 118. However, insome examples, the example OTT device 118 is implemented to interactwith a cloud application programming interface (API) hosted by an HTTPinterface of the census collector 120. Using a cloud API does notrequire implementation of instructions provided as part of an SDK in theOTT device 118. Instead, by using the cloud API, the OTT device 118 isimplemented using an HTTP stack (e.g., libraries and/or other executablecode) that is already present on the OTT device 118. In yet otherexamples, the OTT device 118 itself is not configured to send impressionreporting messages. Instead, apps executed by the OTT device 118 areconfigured to send impression reporting messages based on media access.In some such examples, media publishers (e.g., NBC®, CBS®, etc.) oraggregates (e.g., Hulu®, Direct TV®, etc.) could incorporatefunctionality of the AME-provided SDK in their apps to transmitmonitoring information or impression reporting messages to the censuscollector 120 of the AME when the media accessed via an app executed bythe OTT device 118.

In the illustrated example, data or information (e.g., including the IPaddress 111 of the residential gateway 110), is transmitted to thecensus collector 120 using a Hypertext Transfer Protocol (HTTP) request.While in the illustrated example the example HTTP message is used toconvey the IP address to the census collector, any other approach totransmitting data may additionally or alternatively be used such as, forexample, a file transfer protocol (FTP), HTTP Secure (HTTPS), securesockets layer (SSL), an HTTP Get request. Asynchronous JavaScript andextensible markup language (XML) (AJAX), Simple Mail Transfer Protocol(SMTP) and/or any other network transport protocol that runs, forexample, via Transmission Control Protocol/Internet Protocol (TCP/IP)User Datagram Protocol/Internet Protocol (IP/UDP) and/or any otherInternet-based communication protocols. In the illustrated example, theimpression reporting messages are transmitted in near real-time to thecensus collector 120. As used herein, near real-time is defined to betransmission of data (e.g., impression reporting messages) within ashort time duration (e.g., one minute) of the identification,generation, and/or detection of the data. However, in some examples, thedata may be stored (e.g., cached, buffered, etc.) for a period of timebefore being transmitted to the census collector 120.

Thus, the census collector 120 of the illustrated example identifies theIP address 111 of the residential gateway 110 associated with the OTTdevice 118 when the OTT device 118 provides impression requests. Thecensus collector 120 of the illustrated example communicates, providesand/or otherwise sends the IP address 111 information to theharmonization system 122. The harmonization system 122 of theillustrated example is communicatively coupled to a direct linkagedatabase 126 of the audience measurement system 100. The example directlinkage database 126 is provided or generated by the AME.

As described in greater detail below in connection with FIG. 2A, thedirect linkage database 126 of the illustrated example is provided(e.g., generated, developed, compiled, etc.) during a direct linkagedatabase build phase by the linkage database system 104. The directlinkage database 126 includes a linkage mapping record 127 having dataor information that is organized and/or retrievable using the IP address111. For example, as described in greater detail below in connectionwith FIG. 2A, the direct linkage database 126 includes mappinginformation of the IP address 111, an audience measurement entity (AME)cookie 128, a database proprietor identifier or database proprietor (DP)cookie 130 and/or a database proprietor demographic identifier orhousehold identification (HH ID) 132. In some examples, the directlinkage database 126 of the illustrated example includes an audiencemeasurement entity session identifier (AME session ID) associated withthe AME cookie 128. Thus, based on the received IP address 111, theharmonization system 122 of the illustrated example can retrieve the AMEcookie 128, the DP cookie 130 and/or the HH ID 132 that is assigned toor mapped with the IP address 111 associated with the OTT device 118.The harmonization system 122 of the illustrated example retrieves the HHID 132 associated with the IP address 111 and communicates and/or sends,for example, the 1 i ID 132 to the demographic determiner 124.

The demographic determiner 124 of the impression monitoring system 102retrieves information from a demographic database 134 that associatesthe HH IDs to household demographics. For example, the demographicdeterminer 124 of the illustrated example employs the HH ID 132 torequest demographic information from the demographic database 134associated with the HH ID (i.e., the household ID of the databaseproprietor). For example, the demographic database 134 may includedemographic information of household members associated with the mediapresentation environment 108. The demographic information may include,for example, the number of household members, the age, gender, ethnicityand/or any other information of each household member. Thus, thedemographic determiner 124 receives and/or imports demographicinformation from the demographic database 134 using the HH ID. Thus, thedemographic determiner 124 of the illustrated example assignsdemographic information from the demographic database 134 to the OTTimpression associated with the OTT device 118. In some examples, thedemographic database 134 may employ the DP cookie 130 to provide thedemographic information. In the illustrated example, the demographicdatabase 134 may be obtained from a database proprietor (e.g., thedatabase proprietor 210 of FIGS. 2A and 2B) that collects and maintainshousehold demographic information for a number of households in amonitored population (e.g., a country, a state, a metropolitan area,etc.).

In some examples, a database proprietor may not have household audiencemember composition information (e.g., demographics of household membersin association with the HH ID 132) to associate with the IP address 111.In such examples, the audience measurement entity generates orestablishes an estimated household composition for each IP address thatdoes not have a respective database proprietor household identification.For example, the audience measurement entity establishes an estimatedhousehold composition 137 and assigns the estimated householdcomposition 137 to the IP address 111 (e.g., corresponding to ahousehold for which the database proprietor does not have databaseproprietor household composition information). As described in greaterdetail below, the audience measurement entity establishes the estimatedhousehold composition during an OTT impression monitoring event orduring a build phase of the direct linkage database 126.

As described in greater detail below in connection with FIGS. 2A and 2B,the estimated household composition 137 of the illustrated example maybe determined by using a range of IP addresses assigned to the samegeographic area based on one or more matching characteristic(s) (e.g.,the same set of first three octets of public IP addresses) and/ormatching address allocation block(s) of IP addresses that are providedin the direct linkage database 126.

In some examples, the audience measurement entity of the illustratedexample generates or establishes an estimated household composition ofan unknown household without a database proprietor householdidentification during an OTT impression monitoring event. In some suchinstances, the harmonization system 122 of the illustrated exampleretrieves household identifications (HH IDs) from the direct linkagedatabase 126 associated with corresponding IP addresses that have one ormore matching characteristic(s) or allocation blocks as the IP addressof the unknown household. In turn, the demographic determiner 124 of theillustrated example obtains demographic information from the demographicdatabase 134 using the household identifications retrieved from thedirect linkage database 126 that are mapped to IP addresses havingmatching characteristic(s) with the IP address (e.g., the IP address111) of the unknown household. As described in greater detail below inconnection with FIG. 2A, IP address matching characteristic(s) include,for example, similar octets (e.g., first three octets matching betweenIP addresses), same address allocation block(s), etc. Typically, IPaddresses having matching characteristic(s) (e.g., same first threeoctets across IP addresses or same address allocation block(s)) aretypically assigned to the same geographic region (e.g., a neighborhood,a town, an apartment block, etc.). Thus, in some examples, the estimatedhousehold composition 137 that is determined from known householdidentifications associated with the IP addresses in the direct linkagedatabase 126 that have matching or similar IP address characteristic(s)as the IP address of an unknown household is based on audience membersin households for which the database proprietor has known householdidentifications and for which IP addresses are assigned in the samegeographic region (e.g., a neighborhood, a town, etc.) as the unknownhousehold without a household identification. In some examples, theestimated household composition 137 may be an average or meancomposition of audience members across multiple known households forwhich the database proprietor has known household identifications andfor which IP addresses are assigned in the same geographic region as theunknown household without a household identification. In some examples,the estimated household composition 137 is randomly selected from aplurality of household compositions for which IP addresses are assignedin the same geographic region (e.g., a neighborhood, a town, etc.) asthe household without a household identification.

In the illustrated example of FIG. 1, the estimated householdcomposition 137 is stored in a supplemental household compositiondatabase 135 for use with OTT impression monitoring events performed bythe harmonization system 122. Specifically, the supplemental householdcomposition database 135 of the illustrated example includes an IPaddress-to-estimated household composition mapping 139. In examplesdisclosed herein, the estimated household composition 137 may bedetermined in advance of monitoring impressions such as during thedirect linkage database build phase. In other examples, the estimatedhousehold composition 137 is determined during an OTT impressionmonitoring phase.

For examples, in which the estimated household composition 137 is notgenerated in advance during the direct linkage database build phase, theestimated household composition 137 is generated dynamically during theOTT impression monitoring phase and stored for subsequent use in thesupplemental household composition database 135. In such examples, theharmonization system 122 may use the supplemental household compositiondatabase 135 to determine if an estimated household composition hasalready been established or created for an IP address of an unknownhousehold for which an impression is being logged by the impressionmonitoring system 102. If the harmonization system 122 determines duringan impression collection event that an estimated household composition137 has been generated for an IP address of an unknown household, thedemographic determiner 124 of the illustrated example retrieves theestimated household composition 137 from the supplemental householdcomposition database 135.

As described in greater detail below in connection with FIG. 2B, forexamples in which the supplemental household composition database 135 ispopulated with the estimated household composition 137 during the directlinkage database build phase, the supplemental household compositiondatabase 135 may be built in parallel with the direct linkage database126. For example, an estimated household composition 137 may bedetermined during the direct linkage database build phase for IPaddresses of geographic regions that have a significant population(e.g., over one million residents).

The demographic determiner 124 of the illustrated example includes amodel analyzer 138 (e.g., a viewer assignment model (VAM)) to generatethe media monitoring statistics 119 (e.g., a digital content ratings(DCR) report, a digital advertisement ratings (DAR) report, a digitaltelevision ratings (DTVR) report, etc.) for media presented by the OTTdevice 118. For example, the demographic determiner 124 uses thedemographic information (e.g., via the model analyzer 138) from thedemographic database or the supplemental household composition database135 to establish or determine demographic information of a viewer(s)associated with the OTT device 118. In some examples, the demographicdeterminer 124 of the illustrated example determines or obtainshousehold information from the demographic database 134 or determines orobtains household information from the supplemental householdcomposition database 135. Thus, in some examples, the demographicdeterminer 124 of the illustrated example generates the media monitoringstatistics 119 based on the demographic information provided by thedemographic database 134 (e.g., of a database proprietor) and/or theestimated demographic information provided by the supplemental householdcomposition database 135.

In some examples, the media presentation environment 108 may includemore than one household member. To determine which household member isviewing the media presented by the OTT device 118 and/or to verifyand/or enhance the media monitoring statistics 119 (e.g., enhancedemographic viewership results), the demographic determiner 124 of theillustrated example may use demographic information of panelistsregistered with the AME in combination with the demographic informationprovided by the demographic database 134.

For example, the AME establishes a panel of users who have agreed toprovide their demographic information and to have their media exposureactivities monitored. When a household joins the panel, it providesdetailed information concerning household member composition,identities, and demographics (e.g., genders, ages, ethnicity, income,home location, occupations, etc.) to the AME. For example, thedemographic determiner 124 of the illustrated example may retrieveregistered panelist information from a central facility (e.g., apanelist server) of the AME. In some examples, the census collector 120,for example, may include demographic information of panelists registeredwith the AME (e.g., associated with the AME cookie 128).

Typically, the OTT device 118 is not associated or identified (e.g.,registered) to a panelist registered with the AME. Thus, the demographicdeterminer 124 may compare the demographic information obtained from thedemographic database 134 with AME panel household demographicinformation obtained from the central facility of the AME to identify asimilar AME household (e.g., a similar household composition) as thehousehold demographics provided by the demographic database 134. Forexample, household information (e.g., of its members) provided by thedemographic database 134 may be compared with panelist householdsidentified by the AME to find AME panel households that are similar to(e.g., match) the demographics (e.g., number of members, age, gender,etc.) of the household composition provided by the demographic database134.

In this manner, the demographic determiner 124 of the illustratedexample may employ factors associated with registered householdpanelists of the AME to predict which member(s) of the householdassociated with the household of the demographic database 134 viewed themedia presented by the OTT device 118 (e.g., when more than one personis associated with the media presentation environment 108). For example,using factors such as time of day, content genre, employing the viewingbehavior of the audience measurement entity household panelists, etc.,may be employed to predict which household member(s) identified in thedatabase proprietor information viewed the media presented by the OTTdevice 118. In some examples, the demographic determiner 124 may employa score system to improve the accuracy of the media monitoringstatistics 119. An example method of employing an activity assignmentmodel analyzer is provided in U.S. patent application Ser. No.14/569,474 (Rao et al.), which is incorporated herein by reference inits entirety.

FIG. 2A shows an example linkage database system 104 that may be used toimplement the audience measurement system 100 of FIG. 1. FIG. 2A showsthe example linkage database system 104 of FIG. 1 that is implementedusing a synchronization/exchange process (e.g., a cookiesynchronization/exchange process) between an AME server 204 of an AME201 and a database proprietor 210. The AME server 204 of the illustratedexample includes an example reporting message receiver 203, an exampleAME ID determiner 205, an example IP address identifier 207, an exampleredirect instructor 209, an example database proprietor (DP) messagereporting receiver 211, an example filter 206, an example demographicidentifier retriever 213 (e.g., HH ID 132 retriever), and an examplemapper 215.

As noted above, the linkage database system 104 of the illustratedexample associates or maps the DP cookie 130 and/or the HH ID 132associated with a database proprietor 210 with the IP address 111 of theresidential gateway 110 of the media presentation environment 108 duringa linkage database build phase. For example, the protocols of theInternet 112 (FIG. 1) make cookies inaccessible outside of the domain(e.g., Internet domain, domain name, etc.) on which they were set. Thus,a cookie set, for example, in the client device 116 by the databaseproprietor 210 is accessible to servers in the domain of the databaseproprietor 210, but not to servers outside that domain such as servers(e.g., the census collector 120) in the domain of the AME 201. However,the AME 201 can employ the example linkage database system 104 disclosedherein to access information associated with the DP cookies 130 and/orthe HH ID 132 of the database proprietor 210, which the AME 201 wouldotherwise be unable to access.

Although examples disclosed herein are described as employing cookiessuch as the AME cookie 128 and the DP cookie 130, other types of exampleidentifiers instead of or in addition to cookies may be used as clientdevice identifiers. Examples of other types of identifiers includehardware identifiers (e.g., an international mobile equipment identity(IMEI), a mobile equipment identifier (MEID), a media access control(MAC) address, etc.), an app store identifier (e.g., a Google AndroidID, an Apple ID, an Amazon ID, etc.), an open source unique deviceidentifier (OpenUDID), an open device identification number (ODIN), alogin identifier (e.g., a username), an email address, user agent data(e.g., application type, operating system, software vendor, softwarerevision, etc.), third-party service identifiers (e.g., an “Identifierfor Advertising” (IDFA), advertising service identifiers, device usageanalytics service identifiers, demographics collection serviceidentifiers), web storage data, document object model (DOM) storagedata, local shared objects (also referred to as “Flash cookies”), etc.

Referring to FIG. 2A, to associate or map the IP address 111 with DPcookie 130 and/or the HH ID 132, the linkage database system 104 of theillustrated example builds, formulates or compiles the direct linkagedatabase 126 for use by the impression monitoring system 102 whenmonitoring impressions of the OTT device 118 (FIG. 1) during animpression monitoring phase. As noted above, the linkage database system104 of the illustrated example compiles or maps a profile (e.g., thelinkage mapping record 127) in the direct linkage database 126 withinformation including, for example, the IP address 111 of theresidential gateway 110, the AME cookie 128, the DP cookie 130, the HHID 132 and/or the AME session ID.

To map or associate a cookie (e.g., the DP cookie 130) of a databaseproprietor 210 to the IP address 111 of the residential gateway 110, thelinkage database system 104 of the illustrated example initiates adirect linkage mapping process 400 described in connection with FIG. 2A.Initially, a browser of the client device 116 accesses a web pageincluding an example tag 202 (e.g., beacon instructions). The web pagemay be a webpage that is not associated with the AME. For example, theweb page may include information (e.g., an image or advertisement)included in the web page downloaded via the client device 116. In theillustrated example, the browser of the example client device 116 ofFIG. 1 executes the beacon instructions in the tag 202 and sends acookie reporting message through the residential gateway 110 to theexample AME 201 based on the beacon instructions. In the illustratedexample, the cookie reporting message is transmitted from the clientdevice 116 to the AME server 204 of the AME 201 using a HypertextTransfer Protocol (HTTP) request. Because the HTTP protocol is used, theimpression request includes the AME cookie 128 that identifies a userand/or the client device 116 and the IP address 111 of the residentialgateway 110 that is relaying the HTTP request to the AME server 204. Forexample, the reporting message receiver 203 of the AME server 204 of theillustrated example receives the cookie reporting message from theclient device 116. The AME ID determiner 205 of the illustrated exampleidentifies an AME cookie ID of the AME cookie 128 associated with thecookie reporting message. Additionally, the IP address identifier 207 ofthe illustrated example identifies the IP address (e.g., the public IPaddress 111) associated with the cookie reporting message initiated bythe beacon instructions. In some examples, the AME server 204 isimplemented by the census collector 120 of FIG. 1.

When the reporting message receiver 203 of the AME server 204 of the AME201 receives the cookie reporting message from the client device 116,the mapper 215 of the AME server 204 maps the AME cookie ID of the AMEcookie 128 to the IP address 111 by storing the AME cookie ID togetherwith the IP address 111 in the linkage mapping record 127 of the directlinkage database 126. The redirect instructor 209 of the AME server 204then sends a response or instruction (e.g., a redirect instruction) tothe client device 116 in the form of a redirect response or instruction(e.g., an HTTP 302 redirect) to cause the client device 116 to send asecond cookie reporting message to the database proprietor 210. In theillustrated example, the second cookie reporting message includes the DPcookie 130 and the IP address 111.

When a database proprietor (DP) server 208 of the database proprietor210 receives the second cookie reporting message, the DP server 208retrieves the DP cookie 130 and the IP address 111 from the secondcookie reporting message, and returns a redirect response to the clientdevice 116 to cause the client device 116 to send a third cookiereporting message to the AME 201. For example, the DP message reportingreceiver 211 receives the third cookie reporting message from the DPserver 208. In the illustrated example, the DP server 208 adds a DPcookie ID of the DP cookie 130 to the URL query string of the redirectresponse. In this manner, the client device 116 can send the DP cookieID of the DP cookie 130 to the AME 201 in the third cookie reportingmessage. When the AME server 204 of the AME 201 receives the thirdcookie reporting message from the client device 116, the AME server 204collects the DP cookie ID (e.g., via the DP message reporting receiver211) from the third cookie reporting message and maps (e.g., via themapper 215) the DP cookie ID by storing the IP address 111, the AMEcookie ID, an AME session ID, and the DP cookie ID in the linkagemapping record 127.

To improve accuracy and/or quality of the information in the directlinkage database 126 (e.g., the quality of the IP address 111-to-HH ID132 mapping) and/or to reduce costs and/or limit a number of impressionrequests (e.g., pings) used to build the direct linkage database 126,the linkage database system 104 of the illustrated example filters orleverages tagged media that is flagged as database proprietor enabledmedia (e.g., media enabled for tracking by a database proprietor such asExperian). Thus, in some examples, the linkage database system 104 usesonly database proprietor tag-enabled media to build the direct linkagedatabase 126. Such filtering or leveraging of database proprietorflagged media may improve the accuracy and/or quality of the informationin the direct linkage database 126 (e.g., the quality of the IP address111-to-HH ID 132 mapping).

To filter or leverage database proprietor flagged campaigns to improvethe quality of mapping of the IP address 111 with the AME cookie 128 ofthe AME 201 and/or the DP cookie 130 and/or the HH ID 132, the linkagedatabase system 104 of the illustrated example may employs the filter206. The filter 206 of the illustrated example may be employed to filterlow-quality mapping (IP address-to-cookie) using filter criteria orrules (e.g., filter criteria or rules 1000 shown in FIG. 10). Forexample, the filter 206 of the illustrated example may filter AMEcookies 128 or cookies from the AME 201 that may not be flagged fordatabase proprietor enabled campaigns. Such filtering reduces risk ofassociating IP addresses with incorrect cookies of the AME 201.

For example, the filter 206 of the linkage database system 104 may beused to ignore certain identifiers that may lead to inaccurate mappingbetween the IP address 111, the AME cookie 128, the DP cookie 130 and/orthe HH ID 132. For example, the filter 206 of the illustrated examplemay ignore certain cookies from the AME server 204 and/or the DP server208. In some examples, the filter 206 is not employed and identifiersare used without filtering.

In some examples, the linkage database system 104 of the illustratedexample may ignore identifiers and/or impression requests (e.g., DARpings or cookie reporting messages) associated with an AME cookie thatis not sufficiently old (e.g., an AME cookie that was set by the AME 201within less than a certain time period (e.g., less than 48 hours)).

In some examples, the linkage database system 104 of the illustratedexample may ignore mobile identifiers and/or mobile impression requests(e.g., impressions logged for media accessed via mobile devices andtracked using for example mobile digital ad rating (mDAR) technologies,mobile digital content technologies, etc. to receive mobile impressionreporting messages) associated with mobile web browsers or mobile appsof mobile devices (e.g., which may otherwise provide unreliableresidential IP addresses because impression reporting messages are sentby mobile devices from outside a corresponding household and/or via acellular network (instead of via a residential gateway). For example,the linkage database system 104 of the illustrated example may ignore orfilter mobile impressions associated with mobile web browsers or mobileapps of mobile devices that may be used within the media presentationenvironment 108 to access media via the network and/or the Internet 112or a cellular network. In some examples, the linkage database system 104of the illustrated example may ignore identifiers and/or cookiereporting messages (e.g., mDAR pings) received from users of mobile webbrowsers.

In some examples, the linkage database system 104 of the illustratedexample may ignore identifiers and/or impression requests (e.g., cookiereporting messages) from known business locations (e.g., U.S.businesses) and/or cellular or international IP addresses. Such analysismay be performed using a NetAcuity service or any other geolocationservice(s) or entity. For example. NetAcuity is an IPaddress-to-geolocation third-party service. In some examples, thelinkage database system 104 of the illustrated example may ignoreidentifiers and/or impression requests (e.g., cookie reporting messages)from suspected autonomous devices (e.g., robots, auto-generated webpages, etc.).

In some examples, the linkage database system 104 of the illustratedexample may ignore identifiers and/or impression requests (e.g., cookiereporting messages) from suspected or known non-residential IP addresses(e.g., determined by a number of unique cookies established by theaudience measurement entity). Non-residential IP addresses include, forexample, IP addresses of known businesses (e.g., Starbucks®,neighborhoods using IPv4 NATS, stadiums, etc.). In some examples, thelinkage database system 104 of the illustrated example may ignoreidentifiers and/or requests (e.g., pings) collected or received duringnormal business hours. For example, the linkage database system 104 ofthe illustrated example may ignore identifiers and/or requests (e.g.,pings) received during normal business hours of a Designated Market Area(DMA) determined via, for example, Netacuity. Normal business hours maybe between, for example, 9 a.m. and 5 p.m. of the local time zoneassociated with the DMA. A DMA is a geographical region where apopulation can receive the same (or similar) media offerings.

In some examples, the linkage database system 104 of the illustratedexample may ignore identifiers and/or impression requests (e.g., cookiereporting messages) having AME cookies that have multiple or differentIP addresses and/or pings from secondary IP addresses. In some examples,the linkage database system 104 of the illustrated example may ignoreidentifiers and/or impression requests (e.g., cookie reporting messages)having AME cookies that have less than a threshold number of pings(e.g., 100 pings). In some such examples, the filter 206 may ignore AMEcookies corresponding to less than a threshold number of pings (e.g.,100 pings) after applying one or more of the example foregoing filterparameters. In this manner, the linkage database system 104 of theillustrated example may ignore IP addresses generated by a dynamic hostconfiguration protocol (DHCP) to eliminate households with frequent IPaddress turnover or changes. The foregoing filter criteria arenon-exhaustive and other filter criteria may be employed by the linkagedatabase system 104 to increase the accuracy of mappings of IPaddresses, AME cookies, and DP cookies. In some examples, the AME server204 maps the IP address 111, the AME cookie 128 and the DP cookie 130.In some examples, the AME server 204 of the illustrated example may mapthe IP address 111, the AME cookie 128, the DP cookie 130 and the HH ID132.

With reference now to an example DP household ID mapping process 1100 ofFIG. 11, after the DP cookie 130 is obtained (e.g., based on theforegoing filtered criteria and/or filtered AME cookie 128), thedemographic identifier retriever 213 and/or the AME server 204 uses theDP cookie 130 to identify and import the HH ID 132 associated with theDP cookie 130 from the DP server 208 (block 1102). In the illustratedexample, the DP server 208 maintains a database that maps HH IDs to DPcookies for subscribers of the database proprietor 210 and/or forhouseholds monitored by the database proprietor 210. The DP server 208uses such database to provide HH IDs to the AME 201 for DP cookies thatsurvive the filtering process described above. The AME server 204 maymap DP cookies with respective ones of the database householdidentifiers (HH IDs) periodically (e.g., on an hourly, daily, weekly,monthly basis or any other frequency) or aperiodically (e.g., randomlyor when a criterion is met). In this manner, the AME 201 leverages thismapping after the filtering process to associate HH IDs to correspondingones of the DP cookie IDs that survive the filtering process. Once theHH ID 132 is determined to be associated with the (filtered) DP cookie130, the HH ID 132 is appended to the (filtered) DP cookie 130 andstored in the direct linkage database 126 by the mapper 215 and/or theAME server 204 (block 1104).

The AME server 204 of the illustrated example of FIG. 2A may implementcorrections for IP address-to-DP cookie mappings found to be associatedwith more than one HH ID (block 1106). For example, when ISPs assigndynamic public IP addresses to residential gateways of households usingDHCP, such DHCP process may release and renew assigned public IPaddresses from time to time which changes the public IP addresses of thehouseholds. In some instances, such releasing and renewing of public IPaddresses results in assigning the same IP address to multiplehouseholds at different times within a particular duration (e.g., threehouseholds may be assigned the same IP addresses at different timeswithin a 24-hour period). If the AME server 204 detects that the IPaddress 111 has more than one household identification (e.g., HH IDs)associated with it, then the AME server 204 retains the householdidentification (HH ID) with the greatest number of impression counts forthe IP address-to-DP cookie mapping. The AME server 204 may discardother HH IDs having lower numbers of impression counts. In someexamples, if more than one HH ID includes the same number of greatestimpression counts, the AME server 204 assigns the HH ID (e.g., the HH ID132) with the most recent impression to the IP address-to-DP cookiemapping.

Also in the illustrated example of FIG. 11, the AME server 204 of theillustrated example of FIG. 2A may implement corrections for HH IDsfound to be associated with more than one IP address (block 1108). If anHH ID has more than one IP address associated to it, the AME server 204determines if the IP addresses are from different DMAs (e.g., determinedby Netacuity). For example, the AME server 204 may map the IP addresswith the DMA associated with a geographic location in which the mediapresentation environment 108 is located. In this manner, for aparticular HH ID, the AME server 204 of the illustrated example retainsthe IP address-to-DP cookie mapping with the highest number ofimpression counts and may discard the other IP address-to-DP cookiemappings (corresponding to the same HH ID) having fewer requests. Ifmultiple IP address-to-DP cookie mappings have the same number ofgreatest impression counts, the AME server 204 retains the IPaddress-to-DP cookie mapping with the most recent impression (i.e., intime) for mapping to the HH ID.

As noted above, in some examples, the database proprietor 210 may nothave a household identification (e.g., a HHID) of a household to whichthe IP address 111 is assigned by the ISP 114. In some such examples,the AME 201 and/or the demographic determiner 124 estimates thehousehold composition 137 (e.g., demographics) of the household 108 whenthe IP address 111 of the OTT device 118 cannot be mapped to a householdidentification (e.g., the HH ID 132) of the database proprietor 210. Asnoted above, the AME 201 and/or the demographic determiner 124 establishthe estimated household composition 137 during an OTT impression eventas shown, for example, in FIGS. 1 and 2A and/or the AME 201 and/orduring the build phase of the direct linkage database 126 as shown, forexample, in FIG. 2B.

To determine an estimated household composition (e.g., the estimatedhousehold composition 137) of an unknown household (e.g., the household108) for which the AME 201 cannot map an IP address (e.g., the IPaddress 111) to a household identification (e.g., the HH ID 132) of thedatabase proprietor 210, a demographic estimator (e.g., a demographicestimator of FIG. 2B) of the AME 201 or the harmonization system 122 ofthe illustrated example retrieves known household compositioninformation (e.g., demographics such as, for example, age, gender,household income, primary spoken language (e.g., Spanish, English, etc.)of households corresponding to known database proprietor householdidentifications (e.g., HHIDs) by using the IP address-to-householdidentification mapping provided by the linkage mapping record 127 andthe demographic database 134. For example, the AME 201 or thedemographic determiner 124 establishes an estimated householdcomposition (e.g., demographics) of an unknown household (i.e., anunknown household for which the database proprietor 210 does not have adatabase proprietor household identification) by using one or morehousehold compositions (e.g., demographics) associated with knowndatabase proprietor household identifications in the IPaddress-to-household identification mapping of the linkage mappingrecord 127.

To determine selection of household compositions associated with knownhousehold identifications (HH IDs), the AME 201 leverages knownhousehold compositions of known households that are in a same geographicregion (e.g., a neighborhood, a town, an apartment block, etc.) as theunknown household. In examples disclosed herein, the AME 201 identifiessuch similarly located homes based on blocks of similar public IPaddresses assigned by the ISP 114 to households in a same geographicregion. For example, the AME 201 or the harmonization system 122 of theillustrated example identifies or compares a characteristic(s) of the IPaddress 111 (e.g., of the unknown household for which the databaseproprietor 210 does not have a household identification) to acharacteristic(s) of respective ones of the IP addresses in the IPaddress-to-household identification mapping of the linkage mappingrecord 127 (e.g., household identifications associated with or mapped torespective IP addresses). More specifically, the AME 201 or theharmonization system 122 of the illustrated example identifies the IPaddresses in the IP address-to-household identification mapping of thelinkage mapping record 127 that have similar (e.g., matching) IP addresscharacteristic(s) to the IP address 111 corresponding to the unknownhousehold.

In IPV4 examples, an IP address is composed of four octets (e.g., fourseparate numbers), where each octet can be any value between zero (0)and 255 and the octets are separated by periods (e.g., xxx.xxx.xxx.xxx).In IPv6 examples, an IP address is composed of address allocationblocks. For example, address allocation blocks of an IPv6 addressinclude eight groups of four hexadecimal digits (e.g., each grouprepresents two octets), where each group is separated by a colon. Anexample of an IPv6 address is 2001:0db8:85a3:0000:0000:8a2e:0370: 7334.In IPv6 address examples, matching characteristics of IPv6 addresses mayinclude first three groups, four groups, five groups, six groups orseven groups that match across addresses.

To optimize network administration and utilization. IP addresses areoften allocated to Internet Service Providers (e.g., the ISP 114 ofFIG. 1) by a regional internet registry (RIR) (e.g., American Registryfor Internet Numbers (ARIN), Latin America and Caribbean NetworkInformation Centre (LACNIC). Reseaux IP Europeens Network CoordinationCentre (RIPE NCC). African Network Information Center (AFRINIC) andAsia-Pacific Network Centre (APNIC)) in blocks of contiguous IPaddresses or ranges. In some instances, an ISP (e.g., the ISP 114 ofFIG. 1) assigns IP addresses with the same first three octets (e.g., IPaddresses xxx.xxx.xxx.zzz in the range xxx.xxx.xxx.0 to xxx.xxx.xxx.255;or IPv6 address allocation blocks) to households (e.g., the mediapresentation environment 108) located in the same geographic region(e.g., the same neighborhood, city, apartment block, etc.). For example,a first household in a first city may be assigned a first IP addresshaving the same first three octets as a second IP address assigned to asecond household in the same first city. Thus, the first three octets ofthe first IP address and the first three octets of the second IP addressare a characteristic that can be leveraged by the AME 201 to identifyhouseholds in a same geographic region that can be used to estimate thehousehold composition of an unknown household (e.g., the mediapresentation environment 108). For example, a geographic region may becomposed of households having similar household compositions (e.g.,similar demographics such as age, gender, children, ethnicity, etc.).Thus, in some examples, the AME 201 leverages IP address assignmentstrategies of internet service providers to estimate demographics ofunknown households having IP addresses that do not have existingdatabase proprietor household identifications (e.g., HH ID 132). IPaddress assignment strategies can be verified or determined using an IPaddress to geolocation lookup tool such as, for example,www.geoiptool.com. For example, entering an IP address in a range47.198.12.1 to 47.198.12.255 generates a zip code of 33602 and latitude27.9578 by longitude 82.4622.

Table 1 of FIG. 2C illustrates example household composition informationobtained or retrieved from the database proprietor 210 based on knownhousehold identifications (e.g., HH IDs) that have been mapped to IPaddresses in the IP address-to-household identification mapping of thelinkage mapping record 127 of the direct linkage database 126. Forexample, the AME 201 or the harmonization system 122 of the illustratedexample employs the household identifications (HH IDs) associated withrespective ones of the IP addresses shown in the first column of Table 1to obtain corresponding household composition information (e.g., age andgender information) from the database proprietor 210 shown in thesubsequent columns (e.g., columns 2-15).

Still referring to Table 1 of FIG. 2C, to select household compositioninformation for determining the estimated household composition 137, theAME 201 or the harmonization system 122 of the illustrated examplecompares or analyzes the IP address 111 of an unknown household (e.g.,the media presentation environment 108) to one or more characteristic(s)of the IP addresses shown in Table 1 that have been mapped to databaseproprietor household identifications in the IP address-to-householdidentification mapping of the linkage mapping record 127. For example,the AME 201 or the harmonization system 122 may compare the first threeoctets of the IP address 111 to the first three octets of other IPaddresses shown in Table 1. For example, the AME 201 or the demographicdeterminer 124 retrieves household composition information (e.g.,demographics) from the demographic database 134 and/or the DP server 208for IP addresses in table 1 that have the same IP addresscharacteristic(s) (e.g., the first three octets) as thecharacteristic(s) (e.g., the first three octets) of the IP address 111.

For example, if the IP address 111 of the illustrated example is47.198.12.75, the AME 201 or the harmonization system 122 employs thefirst three octets (i.e., 47.198.12) of the IP address 111 to determinethe estimated household composition 137 of the media presentationenvironment 108. More specifically, the AME 201 of the illustratedexample identifies the IP addresses (e.g., in the IPaddress-to-household identification mapping of the linkage mappingrecord 127) that have the same (e.g., identical) first three octets(i.e., 47.198.12). In the illustrated example, table 1 includes six (6)IP addresses having the same first three octets, which are identified ina box having a dashed line in table 1.

In some examples, to determine the estimated household composition 137,the AME 201 or the demographic determiner 124 of the illustrated exampledetermines (e.g., computes) a mean composition of a household based onthe household composition information of known household identified asdescribed above. For example, the AME 201 or the demographic determiner124 of the illustrated example estimates the household composition ofthe media presentation device 108 by computing an average or meancomposition of the demographics (e.g., number of persons, ages andgenders) corresponding to the known households as described below inconnection with Equation 1 below. For example, the AME 201 or thedemographic determiner 124 of the illustrated example employs householdcomposition information associated with the IP addresses having thefirst three octets of 47.198.12. In some examples, the AME 201 or thedemographic determiner 124 employs rounding technique(s) if the averageor mean of a particular demographic is not a whole number. For example,if the mean value is number that includes a decimal that is equal to orgreater than 0.5, the AME 201 or the demographic determiner 124 of theillustrated example converts the mean value to the nearest higher wholenumber. If the mean value is a number that includes a decimal that isless than 0.5, the AME 201 or the demographic determiner 124 of theillustrated example converts the mean value to the nearest lower wholenumber.

Based on the household composition information associated with theselected IP addresses in Table 1 of FIG. 2C, an estimated householdcomposition of the illustrated example for IP addresses having unknowndatabase proprietor household identifications and are within an IPaddress range of 47.198.12.0-47.198.12.255 includes:

-   -   1 male age of 43, 1 female age of 30; 1 boy age of 14; and no        girls.

For example, the estimated demographic information (e.g., age, gender,adult/child classification, etc.) can be determined by using anaveraging equation such as, for example, equation 1 below.

$\begin{matrix}{{mean} = \frac{\sum\limits_{i = 1}^{i = n}X_{i}}{n}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

Where X_(i) represents ages, number of persons or genders; and n is atotal number samples (e.g., households) used to determine the summationof the values represented by X_(i) to X_(n).

For example, using Equation 1 above, the ages, number of persons andgender demographics of the illustrated example can be estimated asfollows:

-   -   Gender:

${{{Mean}\mspace{14mu}{Adult}\mspace{14mu}{Male}\text{:}\mspace{14mu}\frac{1 + 1 + 2 + 4 + {1\mspace{14mu}{males}}}{6\mspace{14mu}{households}}} = {{8\mspace{14mu}{males}\text{/}6\mspace{14mu}{households}} = 1.25}}{Males},{{{which}\mspace{14mu}{is}\mspace{14mu}{rounded}\mspace{14mu}{down}\mspace{14mu}{to}\mspace{14mu} 1\mspace{14mu}{Male}\mspace{14mu}{per}\mspace{14mu}{unknown}\mspace{14mu}{{household}.{Mean}}\mspace{14mu}{Adult}\mspace{14mu}{Female}\text{:}\mspace{14mu}\frac{1 + 1 + 1 + 1 + {1\mspace{14mu}{females}}}{6\mspace{14mu}{households}}} = {{5\mspace{14mu}{females}\text{/}6\mspace{14mu}{households}} = 0.8}}$${Females},{{{which}\mspace{14mu}{is}\mspace{14mu}{rounded}\mspace{14mu}{up}\mspace{14mu}{to}\mspace{14mu} 1\mspace{14mu}{Female}\mspace{14mu}{per}\mspace{14mu}{unknown}\mspace{14mu}{{household}.\text{}{Mean}}\mspace{11mu}{Child}\mspace{14mu}{Male}\text{:}\mspace{11mu}\frac{1 + 4 + 1}{6\mspace{14mu}{households}}} = {{6\mspace{14mu}{boys}\text{/}6\mspace{14mu}{households}} = {1.0\mspace{14mu}{Boy}\mspace{11mu}{per}\mspace{14mu}{unknown}\mspace{14mu}{household}}}}$${{{Mean}\mspace{14mu}{Child}\mspace{14mu}{Female}\text{:}\mspace{14mu}\frac{1 + 1}{6\mspace{14mu}{households}}} = {{2\mspace{14mu}{girls}\text{/}6\mspace{14mu}{households}} = {0.3\mspace{14mu}{Girls}}}},$

-   -   -   which is rounded down to 0 girls per unknown household.

    -   Age:

${{Male}\mspace{14mu}{Mean}\mspace{14mu}{Age}} = {\frac{45 + 28 + 54 + 20 + 20 + 21 + 22 + 35}{8} = {43\mspace{14mu}{years}\mspace{14mu}{old}\mspace{14mu}{per}\mspace{14mu}{unknown}\mspace{14mu}{household}}}$${{Female}\mspace{14mu}{Mean}\mspace{14mu}{Age}} = {\frac{43 + 28 + 21 + 24 + 35}{5} = {30\mspace{14mu}{years}\mspace{14mu}{hold}\mspace{14mu}{per}\mspace{14mu}{unknown}\mspace{14mu}{household}}}$${{Boy}\mspace{14mu}{Mean}\mspace{14mu}{Age}} = {\frac{15 + 10 + 13 + 15 + 17}{5} = {14\mspace{14mu}{years}\mspace{14mu}{old}\mspace{14mu}{per}\mspace{14mu}{unknown}\mspace{14mu}{{household}.}}}$

Thus, the estimated household composition for IP addresses within the IPaddress range of 47.198.12.0 to 47.198.12.255 that do not have adatabase proprietor household identification is: 1 male age 43, 1 femaleage 30, and 1 boy age 14. In other words, unknown households with an IPaddress range corresponding to a same geographic area as knownhouseholds are assigned the same estimated household composition (e.g.,demographics) determined for the specific IP address range having thesame identified characteristic(s) (e.g., the same first three octets).The AME 201 or the harmonization system 122 stores the estimatedhousehold composition in the supplemental household composition database135 for use by the impression monitoring system 102 of FIG. 1 whenmonitoring OTT impressions.

Alternatively, instead of using an average or mean computation, the AME201 or the demographic determiner 124 of the illustrated example assigns(e.g., randomly assigns) a household composition of a known databaseproprietor household identification from an IP address range of ageographic area to an unknown household that is within the same IPaddress range of the geographic area.

For example, referring to Table 1, if the IP address 111 of thehousehold 108 of the example of FIG. 2A is 47.198.18.1, a householdcomposition associated with a known household identification in the IPaddress range of 47.198.18 (e.g., the same first three octets) may beassigned or selected as the estimated household composition 137 (e.g., arepresentative household composition) of the unknown household. In theillustrated example shown in Table 1, the household compositionassociated with the IP address 47.198.18.194 is selected (e.g.,randomly) as the estimated household composition 137 of the unknownhousehold. Thus, the estimated household composition 137 of the unknownhousehold is: 1 male age 45; 1 female age 47; 1 boy age 15; and 1 girlage 17. In instances where the IP address range of the geographic areaincludes a large number of known household compositions or knowndatabase proprietor household identifications, assigning a randomhousehold composition to the household 108 may provide more accurateresults than, for example, averaging the household composition of theknown household identifications of the IP addresses within that range ofthe geographic area. In some such instances, randomly selecting from alarger number of known household compositions (e.g., the IP addresses inrange 47.198.18) compared to a smaller group of known householdcompositions (e.g., IP addresses in the range 47.198.12) may increasethe probability that the randomly selected household compositionaccurately reflects the household composition of the unknown household.

Referring to FIG. 2A, the linkage database system 104 of the illustratedexample updates the direct linkage database 126 and/or the supplementalhousehold composition database 125 periodically or aperiodically. Forexample, the linkage database system 104 may update the direct linkagedatabase 126 each time the client device 116 accesses a web page thatincludes the tag 202. In some examples, the linkage database system 104of the illustrated example updates the information (e.g., the linkagemapping record 127) of the direct linkage database 126 daily, weekly,monthly, etc.

FIG. 2B shows another example linkage database system 250 that may beused to implement the audience measurement system 100 of FIG. 1. Thosecomponents of the example linkage database system 250 of FIG. 2B thatare substantially similar or identical to the components of the examplelinkage database system 104 described above and that have functionssubstantially similar or identical to the functions of those componentswill not be described in detail again below. Instead, the interestedreader is referred to the above corresponding description. To facilitatethis process, similar reference numbers will be used for likestructures. For example, the linkage database system 250 of theillustrated example includes the AME 201, the direct linkage database126, the IP address-to-HH ID mapping of the linkage mapping record 127,the AME server 204, the database proprietor 210, the DP server 208 andthe AME server 204 including the reporting message receiver 203, the AMEID determiner 205, the IP address identifier 207, the redirectinstructor 209, the DP message reporting receiver 211, the filter 206,the demographic identifier retriever 213, and the mapper 215. Thelinkage database system 250 of the illustrated example includes thesupplemental household composition database 135, the IPaddress-to-estimated household composition mapping 139 and the AMEserver 204 of the illustrated example includes an example demographicestimator 217. The demographic estimator 217 may estimate demographicsof a household when the database proprietor 210 does not include ahousehold/demographic identifier (e.g., an HH ID 132) associated with anIP address mapped to a database proprietor identifier (e.g., a DP cookie130). Unlike the linkage database system 104 of FIGS. 1 and 2A, thelinkage database system 250 of the illustrated example establishes thesupplemental household composition database 135 during the directlinkage database build phase instead of during an OTT impressionmonitoring event as shown in FIGS. 1 and 2A.

While an example manner of implementing the linkage database system 104of FIG. 1 is illustrated in FIGS. 2A and 2B, one or more of theelements, processes and/or devices illustrated in FIGS. 2A and 2B may becombined, divided, re-arranged, omitted, eliminated and/or implementedin any other way. Further, the example reporting message receiver 203,the example AME ID determiner 205, the example IP address identifier207, the example redirect instructor 209, the example DP messagereporting receiver 211, the example filter 206, the example demographicidentifier retriever 213, the example mapper 215, the exampledemographic estimator 217 and/or, more generally, the example linkagedatabase systems 104 and 250 of FIGS. 2A and 2B may be implemented byhardware, software, firmware and/or any combination of hardware,software and/or firmware. Thus, for example, any of the examplereporting message receiver 203, the example AME ID determiner 205, theexample IP address identifier 207, the example redirect instructor 209,the example DP message reporting receiver 211, the example filter 206,the example demographic identifier retriever 213, the example mapper215, the example demographic estimator 217 and/or, more generally, theexample linkage database systems 104 and 250 of FIGS. 2A and 2B could beimplemented by one or more analog or digital circuit(s), logic circuits,programmable processor(s), application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example reportingmessage receiver 203, the example AME ID determiner 205, the example IPaddress identifier 207, the example redirect instructor 209, the exampleDP message reporting receiver 211, the example filter 206, the exampledemographic identifier retriever 213, the example mapper 215, theexample demographic estimator 217 is/are hereby expressly defined toinclude a non-transitory computer readable storage device or storagedisk such as a memory, a digital versatile disk (DVD), a compact disk(CD), a Blu-ray disk, etc. including the software and/or firmware.Further still, the example the example linkage database system 104 ofFIG. 1 may include one or more elements, processes and/or devices inaddition to, or instead of, those illustrated in FIGS. 2A and 2B, and/ormay include more than one of any or all of the illustrated elements,processes and devices.

In examples disclosed herein, means for monitoring impression requestsof an OTT device may be implemented by the example processor 2012 ofFIG. 20. In examples disclosed herein, means for generating Internetprotocol (IP) address-to-cookie mappings may be implemented by theexample reporting message receiver 203, the AME ID determiner 205, theIP address identifier 207, the redirect instruction 209 and/or the DPmessage reporting receiver 211. In examples disclosed herein, means forassociating household identifiers of households with ones of the IPaddress-to-cookie mappings may be implemented by the census collector120, the harmonization system 122, the demographic identifier retriever213 and/or the mapper 215. In examples disclosed herein, means forassociating ones of the household identifiers to logged mediaimpressions based on IP addresses associated with the media impressionsand based on the household identifiers associated with the ones of theIP address-to-cookie mappings may be implemented by demographicretriever 124 and/or the mapper 215. In some examples, the mapper 215provides means for associating the IP addresses corresponding to thehouseholds with cookies corresponding to media accesses from thehouseholds. In examples disclosed herein, the reporting message receiver203 provides means for receiving cookies at the audience measuremententity from client devices (e.g., the client device 116) in thehouseholds (e.g., the media presentation environment 108). In someexamples, the filter 206 provides means for filtering out some of the IPaddress-to-cookie mappings based on characteristics of the IPaddress-to-cookie mappings. In examples disclosed herein, means forassociating demographic information with the logged media impressionsbased on the household identifiers may be implemented by the demographicretriever 124 and/or the demographic identifier retriever 213. In someexamples, means for accessing the demographic information from adatabase based on the household identifiers that is stored in thedatabase in association with the household identifiers may beimplemented by the demographic retriever 124 and/or the demographicidentifier retriever 213. In examples disclosed herein, means foridentifying a characteristic of a first IP address assigned to a firsthousehold without a database proprietor household identification andmeans for identifying second IP addresses of the IP address-to-cookiemappings (e.g., where the second IP addresses identified based on havingthe same characteristic of the first IP address) may be implemented bythe harmonization system 122 and/or the demographic identifier retriever213. In some examples, the demographic retriever 124 and/or thedemographic identifier retriever 213 provide means for retrieving knownhousehold composition information corresponding to second householdsassociated with the second IP addresses. In some examples, thedemographic retriever 124 and/or the demographic estimator 217 providesmeans for estimating a household composition of the first householdassociated with the first IP address based on the known householdcomposition information associated with the second IP addresses. Inexamples disclosed herein, means for computing a mean of the knownhousehold composition information associated with the second IPaddresses and means for randomly selecting from the known householdcomposition information a known household composition associated withone of the second IP addresses are to be implemented by the demographicretriever 124 and/or the demographic estimator 217. In some examples,means for assigning the estimated household composition to each IPaddress that is within a same IP address range as the second IPaddresses may be implemented by the mapper 215.

In some examples, means for building the direct linkage database 127and/or the supplemental database 139 may be implemented by the exampleprocessor 2020 of FIG. 20. In examples disclosed herein, means foraccessing a first reporting message from a client device coupled to aresidential gateway having an internet protocol (IP) address may beimplemented by the reporting message retriever 203. In examplesdisclosed herein, means for assigning an audience measurement entity(AME) identifier to the IP address provided by the received firstreporting message may be implemented by the AME ID determiner 205 and/orthe redirect instructor 209. In examples disclosed herein, means forsending a redirect instruction to the client device to cause the clientdevice to send a second reporting message to a database proprietor(e.g., where the redirect instruction to include the AME identifier andthe IP address) may be implemented by the redirect instructor 209. Inexamples disclosed herein, means for receiving a third reporting messagefrom the database proprietor that includes a database proprietor (DP)identifier may be implemented by the DP message reporting receiver 211.In examples disclosed herein, means for mapping the AME identifier, theIP address and the DP identifier in the linkage mapping record 127 maybe implemented by the mapper 215. In examples disclosed herein, meansfor requesting a household/demographic identifier from the databaseproprietor that is associated with the DP identifier may be implementedby the demographic retriever 124 and/or the demographic identifierretriever 213. In examples disclosed herein, means for storing themapped AME identifier, the IP address, the DP identifier, and thehousehold/demographic identifier in the linkage database 127 may beimplemented by the mapper direct linkage database 126 and/or thenon-volatile memory 2014 of FIG. 20. In examples disclosed herein, meansfor estimating a household composition in response to determining thatthe database proprietor does not have a household/demographic identifiercorresponding to the DP identifier may be implemented by the demographicestimator 217. In examples disclosed herein, means for comparing the IPaddress of the client device with one or more IP addresses of thelinkage mapping record, means for identifying one or more IP addressesof the linkage mapping record having one or more similar characteristicsto the IP address, and means for obtaining one or more databaseproprietor household/demographic identifiers associated with the one ormore identified IP addresses having the one or more similarcharacteristics may be implemented by the demographic identifierretriever 213. In examples disclosed herein, means for mapping the IPaddress with the one or more database proprietor household/demographicidentifiers associated with the one or more IP addresses having the oneor more similar characteristics may be implemented by the mapper 215.

In examples disclosed herein, means for monitoring an impression requestfrom an OTT device may be implemented by the example processor 2020 ofFIG. 20. In some examples, means for receiving an impression requestfrom an over-the-top (OTT) device and/or means for identifying aninternet protocol (IP) address associated with the impression requestprovided by the OTT device may be implemented by the census collector120. In examples disclosed herein, means for accessing a databaseproprietor household/demographic identifier from a linkage mappingrecord that is associated with the identified IP address may beimplemented by the harmonization system 122. In examples disclosedherein, means for requesting demographic information from a databaseproprietor based on the retrieved household/demographic identifierand/or means for associating the requested demographic information basedon the household/demographic identifier to a viewer associated with theOTT device may be implemented by the demographic retriever 124. Inexamples disclosed herein, means for determining if the IP addressassociated with the impression request matches one or more IP addressesmapped in the linkage mapping record may be implemented by theharmonization system 122. In examples disclosed herein, means forestimating a household demographic in response to determining that theIP address associated with the impression request does not match the oneor more IP addresses in the linkage mapping record may be implemented bythe demographic retriever 124. In examples disclosed herein, means foridentifying similar characteristics between the IP address associatedwith the impression request and the IP addresses in the linkage mappingrecord in response to the estimating of the household demographic may beimplemented by the harmonization system 122. In examples disclosedherein, means for obtaining respective ones of database proprietorhousehold/demographic identifiers associated with respective ones of theIP addresses identified with the similar characteristics as the IPaddress associated with the impression request and/or means forrequesting demographic information from the database proprietor based onthe database proprietor household/demographic identifiers may beimplemented by the demographic retriever 124.

Flowcharts representative of example machine readable instructions forimplementing the impression monitoring system 102 and/or the linkagedatabase system 104 and/or 250 of FIGS. 2A and 2B are shown in FIGS.3-19. In these examples, the machine readable instructions implement oneor more programs for execution by a processor such as the processor 2012shown in the example processor platform 2000 discussed below inconnection with FIG. 20. The program(s) may be embodied in softwarestored on a non-transitory computer readable storage medium such as aCD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), aBlu-ray disk, or a memory associated with the processor 2012, but theentirety of the program(s) and/or parts thereof could alternatively beexecuted by a device other than the processor 2012 and/or embodied infirmware or dedicated hardware. Further, although the example program(s)is/are described with reference to the flowcharts illustrated in FIGS.3-7, many other methods of implementing the example linkage databasesystem 104 and/or 250 may alternatively be used. For example, the orderof execution of the blocks may be changed, and/or some of the blocksdescribed may be changed, eliminated, or combined. Additionally oralternatively, any or all of the blocks may be implemented by one ormore hardware circuits (e.g., discrete and/or integrated analog and/ordigital circuitry, a Field Programmable Gate Array (FPGA), anApplication Specific Integrated circuit (ASIC), a comparator, anoperational-amplifier (op-amp), a logic circuit, etc.) structured toperform the corresponding operation without executing software orfirmware.

As mentioned above, the example processes of FIGS. 3-19 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim lists anythingfollowing any form of “include” or “comprise” (e.g., comprises,includes, comprising, including, etc.), it is to be understood thatadditional elements, terms, etc. may be present without falling outsidethe scope of the corresponding claim. As used herein, when the phrase“at least” is used as the transition term in a preamble of a claim, itis open-ended in the same manner as the term “comprising” and“including” are open ended.

FIG. 3 is a flowchart representative of machine readable instructionsfor implementing the audience measurement system 100 of FIGS. 1, 2A and2B. The program of FIG. 3 begins at block 302 when the linkage databasesystem 104 or 250 generates internet protocol address-to-cookiemappings. For example, the linkage database system 104, 250 of theillustrated example creates or generates the linkage mapping record 127(e.g., that includes IP address-to-AME cookie ID-to-DP cookie IDmapping) and/or the supplemental IP address-to-estimated householdcomposition mapping 139 (e.g., that includes estimated demographicassociated with an IP address).

The example linkage database system 104, 250 and/or the example AMEserver 204 then associates household identifiers of ones of thehouseholds with ones of the IP address-to-cookie mappings (block 304).For example, the AME ID determiner 205 determines an AME identifier(e.g., the AME cookie ID 128), the DP message reporting receiver 211determines the database proprietor (DP) identifier (e.g., the DP cookieID 130), and the IP address identifier 207 identifies the IP address 111associated with the AME identifier. The mapper 215 associates the AMEidentifier, the DP identifier and the IP address 111 to form an IPaddress-to-cookie mapping. After the DP identifier is obtained, the AMEserver 204 via, for example, the demographic identifier retriever 213and/or the demographic determiner 124 obtains household/demographicidentifiers (e.g., the HH IDs 132) from the DP server 208 using the DPidentifier provided in the linkage mapping record 127 and/or thesupplemental IP address-to-estimated household composition mapping 139that are associated with the IP address 111. In this manner, the mapper215 can associate household identifiers with the IP address-to-cookiemappings at block 304.

The example impression monitoring system 102 associates ones of thehousehold identifiers to media impressions (block 306). In theillustrated example, the impression monitoring system 102 associates thehousehold/demographic identifiers (e.g., HH IDs) to media impressionslogged for media accessed via over-the-top devices based on IP addressesassociated with the media impressions and based on the associating ofthe household/demographic identifiers with the ones of the IPaddress-to-cookie mappings performed at block 304. For example, thecensus collector 120 identifies the IP address 111 of an impressionassociated with the OTT device 118, the harmonization system 122retrieves a household/demographic identifier (e.g., a HH ID 132) fromthe IP address-to-cookie mappings of the linkage mapping record 127and/or one or more household/demographic identifiers from thesupplemental IP address-to-estimated household composition mapping 139that is assigned to the IP address 111, and the demographic determiner124 may request demographic information from the demographic database134 using the demographic identifiers (e.g., the HH ID 132) associatedwith the IP address 111. In some examples, the harmonization system 122retrieves an estimated demographic household composition 137 from thesupplemental IP address-to-estimated household composition mapping 139that is assigned to the IP address 111.

FIG. 4 is a flowchart representative of machine readable instructionsfor building the direct linkage database 126 and/or the supplementalhousehold composition database 135 of FIGS. 1, 2A and 2B. The processbegins at block 402 when the example reporting message receiver 203 ofthe AME server 204 receives a first reporting message from the clientdevice 116 that is associated with the IP address 111. For example, theclient device 116 may access a website having beacon instructions thatcause the client device 116 to send a reporting message (e.g., a cookiereporting message) through the residential gateway 110 to the exampleAME server 204. In this manner, the reporting message receiver 203 mayreceive the AME cookie 128 in the reporting message from the clientdevice 116.

The example AME ID determiner 205 assigns an audience measurement entitymapping identifier (e.g., an AME cookie ID) to the IP address 111 (block404). For example, the IP address identifier 207 identifies the IPaddress 111 associated with the first reporting message and the AME IDdeterminer 205 assigns an AME cookie ID to the IP address 111 based onthe AME cookie 128 received by the reporting message receiver 203. Theexample redirect instructor 209 locates the AME ID and the IP address111 in a redirect message or instruction (block 406). The exampleredirect instructor 209 sends the redirect message to the client device116 to cause the client device 116 to send a second reporting message tothe database proprietor 210 (block 408). The example DP messagereporting receiver 211 receives a third reporting message from thedatabase proprietor 210 in response to the redirect message thatincludes a database proprietor identifier, the AME ID and the IP address111 (block 410). For example, the database proprietor identifier may bethe DP cookie 130 provided by the database proprietor 210.

The example demographic identifier retriever 213 retrieves a demographicidentifier from the database proprietor 210 using the databaseproprietor identifier (block 412). For example, the demographicidentifier retriever 213 may retrieve the household/demographicidentifier (e.g., the HH ID 132) associated with the DP cookie 130 fromthe database proprietor 210. In some examples, the mapper 215 maps theAME cookie 128, the IP address 111, the DP cookie 130 and the HH ID 132in the linkage mapping record 127.

In some examples, the demographic identifier retriever 213 and/or theAME server 204 determines if the database proprietor 210 includes ahousehold/demographic identifier associated with the DP identifier(block 414). For example, the demographic identifier retriever 213determines if the database proprietor 210 includes an HH ID 132associated with the DP cookie 130. If the demographic identifierretriever 213 determines at block 414 that the database proprietor 210includes a household/demographic identifier associated with the databaseproprietor identifier, the example mapper 215 maps the databaseproprietor identifier, the household/demographic identifier, the AME IDand the IP address 111 (block 416). For example, the mapper 215 maps theAME cookie 128, the IP address 111, the DP cookie 130 and the HH ID 132in the linkage mapping record 127. The example AME server 204 stores themapped database proprietor identifier in association with thehousehold/demographic identifier, the AME ID and the IP address in thedirect linkage database 126 for use by the impression monitoring system102 (block 418).

Referring again to block 414, if the demographic identifier retriever213 determines that the database proprietor 210 does not include ahousehold/demographic identifier associated with the database proprietoridentifier, the example demographic estimator 217 estimates a householdcomposition (block 420). For example, the demographic estimator 217 mayestimate or determine a household composition by using one or morehousehold compositions (e.g., demographics) associated with knowndatabase proprietor household identifications (HH IDs) in the linkagemapping record 127. An example process that may be used to implementblock 420 is described below in connection with FIG. 5. The example AMEserver 204 stores the mapped IP address in association with estimatedhousehold/demographic identifiers in the supplemental linkage database135 (block 422) for use by the impression monitoring system 102.

FIG. 5 is an example process 500 that may implement the example estimatehousehold composition block 420 of FIG. 4. The example demographicestimator 217 compares (e.g., via a comparator) one or more IP addressesof the linkage mapping record 127 to the IP address 111 of the clientdevice (block 502). The example demographic estimator 217 identifies oneor more IP addresses of the linkage mapping record 127 having similarcharacteristics to the IP address 111 (block 504). For example, thedemographic estimator 217 may compare one or more sets of octets (e.g.,the first three octets) or allocation blocks of the IP address 111 toone or more sets of octets (e.g., the first three octets) or allocationblocks of the IP addresses in the linkage mapping record 127. Theexample demographic identifier retriever 213 obtains, requests orretrieves one or more database proprietor household/demographicidentifiers (e.g., HH IDs 132) associated with the identified IPaddresses having one or more similar characteristics (block 506). Forexample, the demographic identifier retriever 213 obtains one or more DPidentifiers (e.g., DP cookies 130) from the linkage mapping record 127associated with the identified one or more IP addresses having similarcharacteristics as the IP address 111 of the client device 116, and thedemographic estimator 217 requests the household/demographic identifiers(e.g., HH IDs 132) from the database proprietor 210 based on the DPidentifiers (e.g., the DP cookies 130). The example mapper 215 maps theIP address 111 of the client device 116 with the one or more databaseproprietor household/demographic identifiers associated with theidentified IP addresses having the similar characteristics to provideestimated household/demographic identifiers (block 508). In this manner,the demographic estimator 217 provides estimated household/demographicidentifiers based on the associated database proprietor identifiers(e.g., the DP cookie IDs 130). In some examples, the demographicestimator 217 does not obtain the household/demographic identifiers(e.g., the HH IDs 132) from the database proprietor 210. Instead, themapper 215 maps the IP address 111 of the client device 116 with the oneor more DP identifiers (e.g., the DP cookies 130) associated with theidentified IP addresses having the similar characteristics. In some suchexamples, the impression monitoring system 102 may obtain one or morehousehold/demographic identifiers using the database proprietoridentifiers from the database proprietor 210 during an impressionmonitoring event.

FIG. 6 illustrates an example process 600 that may be employed by theimpression monitoring system 102 of FIG. 1 to associate demographicswith an impression request from an OTT device. Referring to FIG. 6, theexample census collector 120 receives an impression request from the OTTdevice 118 of the household 108 (block 602). The example censuscollector 120 identifies the IP address 111 associated with the OTTdevice 118 providing the impression request (block 604). The exampleharmonization system 122 compares the IP address 111 to IP addressesstored in the linkage mapping record 127 (block 606). The exampleharmonization system 122 determines if the IP address 111 matches an IPaddress stored in the linkage mapping record 127 (block 608).

If the example harmonization system 122 determines at block 608 that theIP address 111 matches an IP address stored in the linkage mappingrecord 127, the harmonization system 122 retrieves a database proprietorhousehold/demographic identifier associated with the IP address 111 inthe linkage mapping record 127 (block 610). In some examples, thedatabase proprietor household/demographic identifier includes at leastone of the DP cookie 130 or the HH ID 132. The example demographicdeterminer 124 retrieves the demographic information from the databaseproprietor 210 using the database proprietor household/demographicidentifier (block 612). For example, the demographic determiner 124 mayobtain, request or retrieve the demographic information from thedemographic database 134 of the database proprietor 210. The exampledemographic determiner 124 assigns the demographic information from thedatabase proprietor 210 to the OTT impression associated with the OTTdevice 118 that was received by the census collector 120 (block 614).

If the example harmonization system 122 determines at block 608 that theIP address 111 does not match an IP address stored in the linkagemapping record 127, the example harmonization system 122 determines ifthe IP address is in the supplemental household composition database 135(block 616). For example, the supplemental household compositiondatabase 135 may be provided by the linkage database system 104 and/or250 of FIGS. 1, 2A and 2B. If the example harmonization system 122determines that the IP address 111 is in the supplemental householdcomposition database 135 at block 616, the example harmonization system122 retrieves the estimated demographic composition from thesupplemental household composition database 135 (block 618). The exampledemographic determiner 124 assigns the estimated demographic compositionfrom the supplemental household composition database 135 to the OTTimpression associated with the OTT device 118 (block 620).

If the example harmonization system 122 determines at block 616 that theIP address 111 is not in the supplemental household composition database135, the example demographic determiner 124 and/or more generally theAME 201 generates estimated demographic information (block 622). Anexample process that may be used to implement block 622 is describedbelow in connection with FIG. 7. The example demographic determiner 124assigns the established estimated demographic information to the OTTimpression associated with the OTT device 118 (block 624). The exampleprocess of FIG. 6 ends.

FIG. 7 illustrates an example process 700 that may be used to generateestimated demographic information to implement block 622 of FIG. 6. Theexample harmonization system 122 compares the IP address 111 of the OTTdevice 118 to IP addresses of the linkage mapping record 127 (block702). The example harmonization system 122 identifies one or more IPaddresses of the linkage mapping record 127 having similarcharacteristics to the IP address 111 (block 704). For example, theharmonization system 122 may compare one or more sets of octets (e.g.,the first three octets) or allocation blocks of the IP address 111 toone or more sets of octets (e.g., the first three octets) or allocationblocks of the IP addresses in the linkage mapping record 127. Theexample harmonization system 122 may obtain one or more databaseproprietor household/demographic identifiers (e.g., DP cookies 130and/or the HH IDs 132) associated with the identified IP addresseshaving one or more similar characteristics (block 706). For example, theharmonization system 122 may obtain one or more DP identifiers (e.g., DPcookies 130) from the linkage mapping record 127 associated with theidentified one or more IP addresses having similar characteristics asthe IP address 111 of the OTT device 118. The example demographicdeterminer 124 requests or retrieves the demographic information fromthe demographic database 134 of the database proprietor 210 using thehousehold/demographic identifiers (e.g., the DP cookies 130 and/or theHH IDs 132) (block 708). The example demographic determiner 124determines, generates or establishes estimated demographic informationbased on the retrieved demographic information associated with the oneor more database proprietor household/demographic identifiers (block710). In some examples, the demographic determiner 124 may establishestimated demographic information by determining a mean composition of ahousehold associated with the IP address 111 based on a plurality ofhousehold/demographic identifiers mapped to IP addresses of the linkagemapping record 127 having similar IP address characteristics. In someexamples, the demographic determiner 124 may establish estimateddemographic information by randomly selecting a database proprietorhousehold/demographic identifier from an IP address having similarcharacteristics to the IP address 111 of the OTT device 118.

FIGS. 8-19 illustrate examples of an audience measurement system (e.g.,the audience measurement system 100 of FIG. 1). More specifically, theexample implementation is presented with the The Nielsen Company. LLC asthe audience measurement entity (e.g., the AME) and Experian as thedatabase proprietor (e.g., the database proprietor 210). The examples ofFIGS. 8-13 are based on IP addresses assigned to households having knowndatabase proprietor household identifications (e.g., known householdcomposition). The examples of FIGS. 14-19 are based on IP addressesassigned to households that do not have database proprietor householdidentifications (e.g., unknown household composition).

FIG. 8 illustrates an example OTT demographic assignment process 800.The process 800 of FIG. 8 is an overview and each sub-process 1-4referenced in FIG. 8 is illustrated in greater detail in FIGS. 9A, 9B,10, 11, 12A, 12B and 13. To facilitate review, each of the figuresincludes a number identified in a circle that is representative of thesub-processes 1-4 shown in FIG. 8. FIG. 9A illustrates an exampleprocess 900 (e.g., sub-process 1 of FIG. 8) for mapping a residential IPaddresses (e.g., the IP address 111 of FIGS. 1, 2A and 2B) to ExperianCookies (e.g., the DP cookie 130 of FIGS. 1, 2A and 2B). FIG. 9Billustrates an example communication flow to map a residential IPaddress (e.g., the IP address 111 of FIGS. 1, 2A and 2B) to an Experiancookie (e.g., the DP cookie 130 of FIGS. 1, 2A and 2B). FIG. 10illustrates an example process 1000 (e.g., sub-process 2 of FIG. 8) forfiltering out low quality IP address-to-cookie mappings. FIG. 11illustrates an example process 1100 (e.g., sub-process 3 of FIG. 8) formapping the Experian Household ID (e.g., the HH ID 132) to IP addresses(e.g., the IP address 111). FIG. 12A illustrates an example 1300 (e.g.,sub-process 4 of FIG. 8) to assign Experian household demographics toOTT media impressions. FIG. 12B is an example communications flow forassigning household demographics to OTT media impressions. FIG. 13 is anoverall audience measurement system implemented by Nielsen (e.g., theAME 201 of FIG. 2A) and Experian (e.g., the database proprietor 210 ofFIG. 2A).

FIG. 14 is an overview of estimating household composition forhouseholds that do not have Experian household identifications. FIGS.15-19 provide examples of determining an estimated household compositionfor an unknown household based on known household compositions ofhouseholds in the same IP address range as the IP address of the unknownhousehold.

FIG. 20 is a block diagram of an example processor platform 2000 capableof executing instructions to implement the example methods and apparatusdisclosed herein. For example, the processor platform 2000 may implementthe examples shown in FIGS. 1-19. The processor platform 2000 can be,for example, a server, a personal computer, a mobile device (e.g., acell phone, a smart phone, a tablet such as an iPad™), a personaldigital assistant (PDA), an Internet appliance, a DVD player, a CDplayer, a digital video recorder, a Blu-ray player, a gaming console, apersonal video recorder, a set top box, or any other type of computingdevice.

The processor platform 2000 of the illustrated example includes aprocessor 2012. The processor 2012 of the illustrated example ishardware. For example, the processor 2012 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer. The hardware processor may be asemiconductor based (e.g., silicon based) device. In this example, theprocessor 2012 implements the reporting message receiver 203, the AME IDdeterminer 205, the IP address identifier 207, the redirect instructor209, the DP message reporting receiver 211, the filter 206, thedemographic identifier retriever 213, the mapper 215 and/or moregenerally the AME server 204.

The processor 2012 of the illustrated example includes a local memory2013 (e.g., a cache). The processor 2012 of the illustrated example isin communication with a main memory including a volatile memory 2014 anda non-volatile memory 2016 via a bus 2018. The volatile memory 2014 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM). RAMBUS Dynamic Random Access Memory(RDRAM) and/or any other type of random access memory device. Thenon-volatile memory 2016 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 2014,2016 is controlled by a memory controller.

The processor platform 2000 of the illustrated example also includes aninterface circuit 2020. The interface circuit 2020 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 2022 are connectedto the interface circuit 2020. The input device(s) 2022 permit(s) a userto enter data and commands into the processor 2012. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 2024 are also connected to the interfacecircuit 2020 of the illustrated example. The output devices 2024 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 2020 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 2020 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network2026 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 2000 of the illustrated example also includes oneor more mass storage devices 2028 for storing software and/or data.Examples of such mass storage devices 2028 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives. RAIDsystems, and digital versatile disk (DVD) drives.

Coded instructions 2032 to implement the computer readable instructionsrepresented by FIGS. 3-19 may be stored in the mass storage device 2028,in the volatile memory 2014, in the non-volatile memory 2016, and/or ona removable tangible computer readable storage medium such as a CD orDVD. In some examples, the direct linkage database 126 and/or thesupplemental database 139 may be implemented by the volatile memory2014, the non-volatile memory 2016 and/or the mass storage 2028.

Examples disclosed herein enable audience measurement entities tomonitor impression requests from an OTT device using (e.g., only) an IPaddress associated with an OTT device. More specifically, the examplemethods and apparatus disclosed herein enable monitoring impressionsfrom OTT devices without requiring registration of an OTT device.Additionally, example methods and apparatus enable monitoring impressionrequests from OTT devices without requiring a viewer associated with theOTT device to register as a panelist with the audience monitoringentity. In some examples, an audience measurement entity may obtaindemographic composition of a household from which an OTT devicegenerates an impression request without requiring the household toregister with the audience measurement entity and/or without requiringregistration or knowledge of one or more identification features (e.g. adevice ID, a serial number, etc.) associated with the OTT device. Inother words, the examples disclosed herein enable an audiencemeasurement entity to determine demographics associated with animpression request of an OTT device using only an IP address that isassociated with the household from which the OTT device sends theimpression request. Unlike prior techniques, which require registrationof the OTT device and/or a panelist with the audience measurementsystems, examples disclosed herein enable an audience measurement entityto determine demographic composition of a household associated with animpression request from an OTT device without requiring registration ofthe OTT device and/or a viewer/panelist of the household. In someexamples, the audience measurement entity may determine the demographiccomposition of a household associated with an impression request of anOTT device without knowledge of the view of the household.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein and the attached appendices, the scope ofcoverage of this patent is not limited thereto. On the contrary, thispatent covers all methods, apparatus and articles of manufacture fairlyfalling within the scope of the claims of this patent.

What is claimed is:
 1. An audience measurement apparatus comprising: animpression monitoring system to monitor and log media impressions basedon impression requests received via network communications fromover-the-top devices; a linkage database system to implement theimpression monitoring system; a processor in circuit with the impressionmonitoring system, the processor to: access first cookie reportingmessages received at an audience measurement entity (AME) based onbeacon instructions executed at client devices associated withhouseholds through residential gateways associated with households, thefirst cookie reporting messages including AME cookies; identify InternetProtocol (IP) addresses of the residential gateways provided in thefirst cookie reporting messages; associate ones of the AME cookies ofthe first cookie reporting messages with respective ones of the IPaddresses identified in the first cookie reporting messages; send secondcookie reporting messages to a database proprietor (DP) via firstredirect instructions provided to respective ones of the client devices;access third cookie reporting messages received at the audiencemeasurement entity based on second redirect responses provided to onesof the client devices by the database proprietor, the third cookiereporting messages including the IP addresses and DP cookies associatedwith household identifiers of the households, the household identifiersgenerated by the database proprietor, the database proprietor beingseparate from a media delivery service that provides media associatedwith the media impressions; generate IP address-to-cookie mappings basedon the IP address, the AME cookies, and the DP cookies; associate thehousehold identifiers of the households with ones of the IPaddress-to-cookie mappings; and associate ones of the householdidentifiers to the logged media impressions based on IP addressesassociated with the media impressions and based on the householdidentifiers associated with the ones of the IP address-to-cookiemappings.
 2. The apparatus as defined by claim 1, further including aharmonization system to associate the IP addresses corresponding to thehouseholds with at least one of the AME cookies or the DP cookiescorresponding to media accesses from the households to generate the IPaddress-to-cookie mappings.
 3. The apparatus as defined by claim 1,wherein the database proprietor is in a first Internet domain separatefrom a second Internet domain of the audience measurement entity thatgenerates the IP address-to-cookie mappings.
 4. The apparatus as definedin claim 3, wherein the client devices are different from theover-the-top devices.
 5. The apparatus as defined in claim 1, furtherincluding a demographic retriever to associate demographic informationwith the logged media impressions based on the household identifiers. 6.The apparatus as defined in claim 5, further including accessing thedemographic information from a database based on the householdidentifiers, the demographic information stored in the database inassociation with the household identifiers.
 7. An apparatus as definedin claim 1, wherein the processor is to: identify a characteristic of afirst IP address assigned to a first household without a databaseproprietor household identification; identify second IP addresses of theIP address-to-cookie mappings, the second IP addresses identified basedon having the same characteristic of the first IP address; retrieveknown household composition information corresponding to secondhouseholds associated with the second IP addresses; and estimate ahousehold composition of the first household associated with the firstIP address based on the known household composition informationassociated with the second IP addresses.
 8. The apparatus as defined inclaim 7, wherein the processor is to assign the estimated householdcomposition to each IP address that is within a same IP address range asthe second IP addresses.
 9. A non-transitory computer readable mediumcomprising instructions that, when executed, cause at least oneprocessor to at least: access first cookie reporting messages receivedat an audience measurement entity (AME) based on beacon instructionsexecuted at client devices associated with households throughresidential gateways associated with households, the first cookiereporting messages including AME cookies; identify Internet Protocol(IP) addresses of the residential gateways provided in the first cookiereporting messages; associate ones of the AME cookies of the firstcookie reporting messages with respective ones of the IP addressesidentified in the first cookie reporting messages; send second cookiereporting messages to a database proprietor (DP) via first redirectinstructions provided to respective ones of the client devices; accessthird cookie reporting messages received at the audience measuremententity based on second redirect responses provided to ones of the clientdevices by the database proprietor, the third cookie reporting messagesincluding the IP addresses and DP cookies associated with householdidentifiers of the households, the household identifiers generated bythe database proprietor, the database proprietor being separate from amedia delivery service that provides media associated with the mediaimpressions; generate IP address-to-cookie mappings based on the IPaddress, the AME cookies, and the DP cookies; associate the householdidentifiers of the households with ones of the IP address-to-cookiemappings; and associate ones of the household identifiers to loggedmedia impressions based on IP addresses associated with the mediaimpressions and based on the household identifiers associated with theones of the IP address-to-cookie mappings.
 10. The non-transitorycomputer readable medium as defined in claim 9, wherein the instructionsare further to cause the at least one processor to, associate the IPaddresses corresponding to the households with at least one of the AMEcookies or the DP cookies corresponding to media accesses from thehouseholds to generate the IP address-to-cookie mappings.
 11. Thenon-transitory computer readable medium as defined in claim 9, whereinthe database proprietor is in a first Internet domain separate from asecond Internet domain of an audience measurement entity that generatesthe IP address-to-cookie mappings.
 12. The non-transitory computerreadable medium as defined in claim 11, wherein the client devices aredifferent from over-the-top devices.
 13. The non-transitory computerreadable medium as defined in claim 9, wherein the instructions arefurther to cause the at least one processor to, associate demographicinformation with the logged media impressions based on the householdidentifiers.
 14. The non-transitory computer readable medium as definedin claim 13, wherein the instructions are further to cause the at leastone processor to, access the demographic information from a databasebased on the household identifiers, the demographic information storedin the database in association with the household identifiers.
 15. Thenon-transitory computer readable medium as defined in claim 9, whereinthe instructions are further to cause the at least one processor to,identify a characteristic of a first IP address assigned to a firsthousehold without a database proprietor household identification;identify second IP addresses of the IP address-to-cookie mappings, thesecond IP addresses identified based on having the same characteristicof the first IP address; retrieve known household compositioninformation corresponding to second households associated with thesecond IP addresses; and estimate a household composition of the firsthousehold associated with the first IP address based on the knownhousehold composition information associated with the second IPaddresses.
 16. The non-transitory computer readable medium as defined inclaim 15, wherein the instructions are further to cause the at least oneprocessor to, assign the estimated household composition to each IPaddress that is within a same IP address range as the second IPaddresses.
 17. A method comprising: accessing, by executing aninstruction with a processor, first cookie reporting messages receivedat an audience measurement entity (AME) based on beacon instructionsexecuted at client devices associated with households throughresidential gateways associated with households, the first cookiereporting messages including AME cookies; identifying, by executing aninstruction with the processor, Internet Protocol (IP) addresses of theresidential gateways provided in the first cookie reporting messages;associating, by executing an instruction with the processor, ones of theAME cookies of the first cookie reporting messages with respective onesof the IP addresses identified in the first cookie reporting messages;sending, by executing an instruction with the processor, second cookiereporting messages to a database proprietor (DP) via first redirectinstructions provided to respective ones of the client devices;accessing, by executing an instruction with the processor, third cookiereporting messages received at the audience measurement entity based onsecond redirect responses provided to ones of the client devices by thedatabase proprietor, the third cookie reporting messages including theIP addresses and DP cookies associated with household identifiers, thehousehold identifiers generated by the database proprietor, the databaseproprietor being separate from a media delivery service that providesmedia associated with the media impressions; generating, by executing aninstruction with the processor, IP address-to-cookie mappings based onthe IP address, the AME cookies, and the DP cookies; associating, byexecuting an instruction with the processor, the household identifiersof the households with ones of the IP address-to-cookie mappings; andassociating, by executing an instruction with the processor, ones of thehousehold identifiers to logged media impressions based on IP addressesassociated with the media impressions and based on the householdidentifiers associated with the ones of the IP address-to-cookiemappings.
 18. The method as defined by claim 17, wherein generating theIP address-to-cookie mappings includes associating the IP addressescorresponding to the households with at least one of the AME cookies orthe DP cookies corresponding to media accesses from the households. 19.The method as defined by claim 17, wherein the database proprietor is ina first Internet domain separate from a second Internet domain of anaudience measurement entity that generates the IP address-to-cookiemappings.
 20. The method as defined in claim 19, wherein the clientdevices are different from over-the-top devices.
 21. The method asdefined in claim 17, further including associating demographicinformation with the logged media impressions based on the householdidentifiers.
 22. The method as defined in claim 21, further includingaccessing the demographic information from a database based on thehousehold identifiers, the demographic information to be stored in thedatabase in association with the household identifiers.
 23. A method asdefined in claim 17, further including: identifying, by executing aninstruction with the processor, a characteristic of a first IP addressassigned to a first household without a database proprietor householdidentification; identifying, by executing an instruction with theprocessor, second IP addresses of the IP address-to-cookie mappings, thesecond IP addresses identified based on having the same characteristicof the first IP address; retrieving, by executing an instruction withthe processor, known household composition information corresponding tosecond households associated with the second IP addresses; andestimating a household composition of the first household associatedwith the first IP address based on the known household compositioninformation associated with the second IP addresses.
 24. The method asdefined in claim 23, further including assigning the estimated householdcomposition to each IP address that is within a same IP address range asthe second IP addresses.